'''simple docstring'''




import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
__UpperCAmelCase					=logging.get_logger("transformers.models.speecht5")

__UpperCAmelCase					={
    "speech_encoder_prenet.layer_norm": "speecht5.encoder.prenet.feature_projection.layer_norm",
    "speech_encoder_prenet.post_extract_proj": "speecht5.encoder.prenet.feature_projection.projection",
    "speech_encoder_prenet.pos_conv.0": "speecht5.encoder.prenet.pos_conv_embed.conv",
    "speech_encoder_prenet.mask_emb": "speecht5.encoder.prenet.masked_spec_embed",
}
__UpperCAmelCase					={
    "text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
    "text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
__UpperCAmelCase					={
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0": "speecht5.decoder.prenet.layers.0",
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0": "speecht5.decoder.prenet.layers.1",
    "speech_decoder_prenet.decoder_prenet.0.1": "speecht5.decoder.prenet.final_layer",
    "speech_decoder_prenet.decoder_prenet.1.alpha": "speecht5.decoder.prenet.encode_positions.alpha",
    "speech_decoder_prenet.spkembs_layer.0": "speecht5.decoder.prenet.speaker_embeds_layer",
}
__UpperCAmelCase					={
    "speech_decoder_postnet.feat_out": "speech_decoder_postnet.feat_out",
    "speech_decoder_postnet.prob_out": "speech_decoder_postnet.prob_out",
    "speech_decoder_postnet.postnet.postnet.0.0": "speech_decoder_postnet.layers.0.conv",
    "speech_decoder_postnet.postnet.postnet.0.1": "speech_decoder_postnet.layers.0.batch_norm",
    "speech_decoder_postnet.postnet.postnet.1.0": "speech_decoder_postnet.layers.1.conv",
    "speech_decoder_postnet.postnet.postnet.1.1": "speech_decoder_postnet.layers.1.batch_norm",
    "speech_decoder_postnet.postnet.postnet.2.0": "speech_decoder_postnet.layers.2.conv",
    "speech_decoder_postnet.postnet.postnet.2.1": "speech_decoder_postnet.layers.2.batch_norm",
    "speech_decoder_postnet.postnet.postnet.3.0": "speech_decoder_postnet.layers.3.conv",
    "speech_decoder_postnet.postnet.postnet.3.1": "speech_decoder_postnet.layers.3.batch_norm",
    "speech_decoder_postnet.postnet.postnet.4.0": "speech_decoder_postnet.layers.4.conv",
    "speech_decoder_postnet.postnet.postnet.4.1": "speech_decoder_postnet.layers.4.batch_norm",
}
__UpperCAmelCase					={
    "text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
__UpperCAmelCase					={
    "text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
__UpperCAmelCase					={
    "encoder.layers.*.self_attn.k_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj",
    "encoder.layers.*.self_attn.v_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj",
    "encoder.layers.*.self_attn.q_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj",
    "encoder.layers.*.self_attn.out_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj",
    "encoder.layers.*.self_attn_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.layer_norm",
    "encoder.layers.*.fc1": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense",
    "encoder.layers.*.fc2": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense",
    "encoder.layers.*.final_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "speecht5.encoder.wrapped_encoder.layer_norm",
    "encoder.pos_emb.pe_k": "speecht5.encoder.wrapped_encoder.embed_positions.pe_k",
}
__UpperCAmelCase					={
    "decoder.layers.*.self_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj",
    "decoder.layers.*.self_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj",
    "decoder.layers.*.self_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj",
    "decoder.layers.*.self_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj",
    "decoder.layers.*.self_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm",
    "decoder.layers.*.encoder_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj",
    "decoder.layers.*.encoder_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj",
    "decoder.layers.*.encoder_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj",
    "decoder.layers.*.encoder_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj",
    "decoder.layers.*.encoder_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm",
    "decoder.layers.*.fc1": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense",
    "decoder.layers.*.fc2": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense",
    "decoder.layers.*.final_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm",
}
__UpperCAmelCase					={
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
__UpperCAmelCase					={
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__UpperCAmelCase					={
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__UpperCAmelCase					=[]
__UpperCAmelCase					=[
    "encoder.version",
    "encoder.layers.*.norm_k.weight",
    "encoder.layers.*.norm_k.bias",
    "decoder.version",
    "decoder.layers.*.norm_k.weight",
    "decoder.layers.*.norm_k.bias",
    "decoder.pos_emb.pe_k",
    "speech_encoder_prenet.embed_positions._float_tensor",
    "text_decoder_prenet.embed_positions._float_tensor",
]
__UpperCAmelCase					=IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "speech_decoder_prenet.*",
    "speech_decoder_postnet.*",
]
__UpperCAmelCase					=IGNORE_KEYS + [
    "encoder.proj",
    "speech_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
__UpperCAmelCase					=IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Any:
    for attribute in key.split('''.'''							):
        __lowerCamelCase            =    getattr(UpperCamelCase__							,    UpperCamelCase__							)

    if weight_type is not None:
        __lowerCamelCase            =    getattr(UpperCamelCase__							,    UpperCamelCase__							).shape
    else:
        __lowerCamelCase            =    hf_pointer.shape

    if hf_shape != value.shape:
        raise ValueError(
            f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
            f""" {value.shape} for {full_name}"""							)

    if weight_type == "weight":
        __lowerCamelCase            =    value
    elif weight_type == "weight_g":
        __lowerCamelCase            =    value
    elif weight_type == "weight_v":
        __lowerCamelCase            =    value
    elif weight_type == "bias":
        __lowerCamelCase            =    value
    elif weight_type == "running_mean":
        __lowerCamelCase            =    value
    elif weight_type == "running_var":
        __lowerCamelCase            =    value
    elif weight_type == "num_batches_tracked":
        __lowerCamelCase            =    value
    else:
        __lowerCamelCase            =    value

    logger.info(f"""{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}."""							)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       List[str]:
    for key in ignore_keys:
        if key.endswith('''.*'''							):
            if name.startswith(key[:-1]							):
                return True
        elif ".*." in key:
            __lowerCamelCase      ,    __lowerCamelCase            =    key.split('''.*.'''							)
            if prefix in name and suffix in name:
                return True
        elif key in name:
            return True
    return False






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Dict:
    __lowerCamelCase            =    []

    if task == "s2t":
        __lowerCamelCase            =    hf_model.speechta.encoder.prenet.feature_encoder
        __lowerCamelCase            =    MAPPING_S2T
        __lowerCamelCase            =    IGNORE_KEYS_S2T
    elif task == "t2s":
        __lowerCamelCase            =    None
        __lowerCamelCase            =    MAPPING_T2S
        __lowerCamelCase            =    IGNORE_KEYS_T2S
    elif task == "s2s":
        __lowerCamelCase            =    hf_model.speechta.encoder.prenet.feature_encoder
        __lowerCamelCase            =    MAPPING_S2S
        __lowerCamelCase            =    IGNORE_KEYS_S2S
    else:
        raise ValueError(f"""Unsupported task: {task}"""							)

    for name, value in fairseq_dict.items():
        if should_ignore(UpperCamelCase__							,    UpperCamelCase__							):
            logger.info(f"""{name} was ignored"""							)
            continue

        __lowerCamelCase            =    False
        if "conv_layers" in name:
            load_conv_layer(
                UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    hf_model.config.feat_extract_norm == '''group'''							,    )
            __lowerCamelCase            =    True
        else:
            for key, mapped_key in MAPPING.items():
                # mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

                if "*" in key:
                    __lowerCamelCase      ,    __lowerCamelCase            =    key.split('''.*.'''							)
                    if prefix in name and suffix in name:
                        __lowerCamelCase            =    suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                if key in name:
                    __lowerCamelCase            =    True
                    if "*" in mapped_key:
                        __lowerCamelCase            =    name.split(UpperCamelCase__							)[0].split('''.'''							)[-2]
                        __lowerCamelCase            =    mapped_key.replace('''*'''							,    UpperCamelCase__							)
                    if "weight_g" in name:
                        __lowerCamelCase            =    '''weight_g'''
                    elif "weight_v" in name:
                        __lowerCamelCase            =    '''weight_v'''
                    elif "bias" in name:
                        __lowerCamelCase            =    '''bias'''
                    elif "weight" in name:
                        __lowerCamelCase            =    '''weight'''
                    elif "running_mean" in name:
                        __lowerCamelCase            =    '''running_mean'''
                    elif "running_var" in name:
                        __lowerCamelCase            =    '''running_var'''
                    elif "num_batches_tracked" in name:
                        __lowerCamelCase            =    '''num_batches_tracked'''
                    else:
                        __lowerCamelCase            =    None
                    set_recursively(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
                continue
        if not is_used:
            unused_weights.append(UpperCamelCase__							)

    logger.warning(f"""Unused weights: {unused_weights}"""							)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Any:
    __lowerCamelCase            =    full_name.split('''conv_layers.'''							)[-1]
    __lowerCamelCase            =    name.split('''.'''							)
    __lowerCamelCase            =    int(items[0]							)
    __lowerCamelCase            =    int(items[1]							)

    if type_id == 0:
        if "bias" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                raise ValueError(
                    f"""{full_name} has size {value.shape}, but"""
                    f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""							)
            __lowerCamelCase            =    value
            logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""							)
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                raise ValueError(
                    f"""{full_name} has size {value.shape}, but"""
                    f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""							)
            __lowerCamelCase            =    value
            logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""							)
    elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
        if "bias" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                raise ValueError(
                    f"""{full_name} has size {value.shape}, but"""
                    f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""							)
            __lowerCamelCase            =    value
            logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""							)
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                raise ValueError(
                    f"""{full_name} has size {value.shape}, but"""
                    f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""							)
            __lowerCamelCase            =    value
            logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""							)
    else:
        unused_weights.append(UpperCamelCase__							)






@torch.no_grad()
def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__=None							,    UpperCamelCase__=None							,    UpperCamelCase__=None							,    )      ->       str:
    if config_path is not None:
        __lowerCamelCase            =    SpeechTaConfig.from_pretrained(UpperCamelCase__							)
    else:
        __lowerCamelCase            =    SpeechTaConfig()

    if task == "s2t":
        __lowerCamelCase            =    config.max_text_positions
        __lowerCamelCase            =    SpeechTaForSpeechToText(UpperCamelCase__							)
    elif task == "t2s":
        __lowerCamelCase            =    18_76
        __lowerCamelCase            =    6_00
        __lowerCamelCase            =    config.max_speech_positions
        __lowerCamelCase            =    SpeechTaForTextToSpeech(UpperCamelCase__							)
    elif task == "s2s":
        __lowerCamelCase            =    18_76
        __lowerCamelCase            =    config.max_speech_positions
        __lowerCamelCase            =    SpeechTaForSpeechToSpeech(UpperCamelCase__							)
    else:
        raise ValueError(f"""Unknown task name: {task}"""							)

    if vocab_path:
        __lowerCamelCase            =    SpeechTaTokenizer(UpperCamelCase__							,    model_max_length=config.max_text_positions							)

        # Mask token behaves like a normal word, i.e. include the space before it
        __lowerCamelCase            =    AddedToken('''<mask>'''							,    lstrip=UpperCamelCase__							,    rstrip=UpperCamelCase__							)
        __lowerCamelCase            =    mask_token
        tokenizer.add_special_tokens({'''mask_token''': mask_token}							)
        tokenizer.add_tokens(['''<ctc_blank>''']							)

    __lowerCamelCase            =    SpeechTaFeatureExtractor()
    __lowerCamelCase            =    SpeechTaProcessor(tokenizer=UpperCamelCase__							,    feature_extractor=UpperCamelCase__							)
    processor.save_pretrained(UpperCamelCase__							)

    __lowerCamelCase            =    torch.load(UpperCamelCase__							)
    recursively_load_weights(fairseq_checkpoint['''model''']							,    UpperCamelCase__							,    UpperCamelCase__							)

    model.save_pretrained(UpperCamelCase__							)

    if repo_id:
        print('''Pushing to the hub...'''							)
        processor.push_to_hub(UpperCamelCase__							)
        model.push_to_hub(UpperCamelCase__							)


if __name__ == "__main__":
  __UpperCAmelCase					=argparse.ArgumentParser()
  parser.add_argument(
      "--task",
      default="s2t",
      type=str,
      help="Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.",
  )
  parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to fairseq checkpoint")
  parser.add_argument("--vocab_path", default=None, type=str, help="Path to SentencePiece model")
  parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
  parser.add_argument(
      "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
  )
  parser.add_argument(
      "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
  )

  __UpperCAmelCase					=parser.parse_args()
  convert_speechta_checkpoint(
      args.task,
      args.checkpoint_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.vocab_path,
      args.push_to_hub,
  )

'''simple docstring'''




import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch




@require_torch
class        a__ :






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						str       ):





         """simple docstring"""



         return self.get_dummy_input()






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						str       ):





         """simple docstring"""



         if self.block_type == "down":
             return (4, 32, 16, 16)
         elif self.block_type == "mid":
             return (4, 32, 32, 32)
         elif self.block_type == "up":
             return (4, 32, 64, 64)

         raise ValueError(f"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."""       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]    ,							a						:						List[Any]=True    ,							a						:						Dict=False    ,							a						:						Optional[Any]=False    ,							a						:						List[Any]=False    ,							):





         """simple docstring"""



         __lowerCamelCase            =    4
         __lowerCamelCase            =    32
         __lowerCamelCase            =    (32, 32)

         __lowerCamelCase            =    torch.manual_seed(0       )
         __lowerCamelCase            =    torch.device(a       )
         __lowerCamelCase            =    (batch_size, num_channels) + sizes
         __lowerCamelCase            =    randn_tensor(a    ,							generator=a    ,							device=a       )
         __lowerCamelCase            =    {'''hidden_states''': hidden_states}

         if include_temb:
             __lowerCamelCase            =    1_28
             __lowerCamelCase            =    randn_tensor((batch_size, temb_channels)    ,							generator=a    ,							device=a       )

         if include_res_hidden_states_tuple:
             __lowerCamelCase            =    torch.manual_seed(1       )
             __lowerCamelCase            =    (randn_tensor(a    ,							generator=a    ,							device=a       ),)

         if include_encoder_hidden_states:
             __lowerCamelCase            =    floats_tensor((batch_size, 32, 32)       ).to(a       )

         if include_skip_sample:
             __lowerCamelCase            =    randn_tensor(((batch_size, 3) + sizes)    ,							generator=a    ,							device=a       )

         return dummy_input






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]       ):





         """simple docstring"""



         __lowerCamelCase            =    {
             '''in_channels''': 32,
             '''out_channels''': 32,
             '''temb_channels''': 1_28,
         }
         if self.block_type == "up":
             __lowerCamelCase            =    32

         if self.block_type == "mid":
             init_dict.pop('''out_channels'''       )

         __lowerCamelCase            =    self.dummy_input
         return init_dict, inputs_dict






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]    ,							a						:						List[str]       ):





         """simple docstring"""



         __lowerCamelCase      ,    __lowerCamelCase            =    self.prepare_init_args_and_inputs_for_common()
         __lowerCamelCase            =    self.block_class(**a       )
         unet_block.to(a       )
         unet_block.eval()

         with torch.no_grad():
             __lowerCamelCase            =    unet_block(**a       )

         if isinstance(a    ,							a       ):
             __lowerCamelCase            =    output[0]

         self.assertEqual(output.shape    ,							self.output_shape       )

         __lowerCamelCase            =    output[0, -1, -3:, -3:]
         __lowerCamelCase            =    torch.tensor(a       ).to(a       )
         assert torch_all_close(output_slice.flatten()    ,							a    ,							atol=5e-3       )






     @unittest.skipIf(torch_device == '''mps'''    ,							'''Training is not supported in mps'''       )
     def   SCREAMING_SNAKE_CASE__  (					self						:						Any       ):





         """simple docstring"""



         __lowerCamelCase      ,    __lowerCamelCase            =    self.prepare_init_args_and_inputs_for_common()
         __lowerCamelCase            =    self.block_class(**a       )
         model.to(a       )
         model.train()
         __lowerCamelCase            =    model(**a       )

         if isinstance(a    ,							a       ):
             __lowerCamelCase            =    output[0]

         __lowerCamelCase            =    torch.device(a       )
         __lowerCamelCase            =    randn_tensor(output.shape    ,							device=a       )
         __lowerCamelCase            =    torch.nn.functional.mse_loss(a    ,							a       )
         loss.backward()
